DFT-ML-Based Property Prediction of Transition Metal Complex Photosensitizers for Photodynamic Therapy

Photodynamic therapy (PDT) is a noninvasive clinical treatment for cancers using photosensitizers and light. While most research has focused on organic molecules, such as porphyrins as photosensitizers, there is emerging interest in the utilization of transition metal complexes (TMCs). Photosensitizer synthesis and the following performance test are time- and resource-consuming, so presynthetic screening of photosensitizers for their property would be critical. In this work, a hybrid mechanistic and data-driven model is proposed for the quantitative structure-property relationship (QSPR) of photosensitizers; important excited-state quantum chemistry descriptors (e.g., excitation energy) are first calculated based on density functional theory (DFT), and these descriptors, together with other molecular descriptors, are used to build single and hybrid machine learning (ML) models for the prediction of the singlet oxygen quantum yield of hexacoordinate TMC photosensitizers (Ru-, Ir-, and Re-complex). The support vector regression model and kernel ridge regression model are shown to provide good predictions on test (<i>R</i> ² > 0.9) and external test sets (<i>R</i> ² > 0.7) in single-ML models, while the delta-learning model and the Mixture-of-Experts model can further improve the generalization ability (<i>R</i> ² up to 0.87 on the external test set) and show strong universality. SHAP analysis further confirms the reasonable choice of the mechanistic descriptors in the QSPR model. To our knowledge, this constitutes the first integrated DFT-ML framework specifically designed for the unique challenges of small data sets in TMC photosensitizer research.